Neutrophil-to-Lymphocyte Ratio Cut-Off Point for COVID-19 Mortality: A Retrospective Study

Several months have passed since the onset of the COVID-19 pandemic. Multiple characteristics have been proposed as prognostic factors so far. This study aims to provide evidence on the association of neutrophil-to-lymphocyte ratio (NLR) at the hospitalization time and three desired outcomes (mortality, prolonged hospitalization, and intensive care unit [ICU] admission). We designed a single-centre retrospective observational study in Baharloo Hospital (Tehran, Iran) from 20 February to 19 April 2020. Patients with confirmed COVID-19 diagnosis via rt-PCR or chest CT imaging were included. Demographic and clinical data were obtained. The sample was divided into three groups, using tertile boundaries of initial NLR. The differences in mortality, comorbidities, hospitalization duration, drug administration, and ICU admission between these three groups were investigated. The identified confounding factors were adjusted to calculate the odds ratio of death, ICU admission, and prolonged hospitalization. Nine hundred sixty-three patients were included. In total, 151 and 212 participants experienced mortality and ICU admission, respectively. In multivariate logistic regression models, the adjusted odds ratio for mortality event in the second and third tertile of initial NLR after full adjustment were 1.89 (95% CI:1.07-3.32) and 2.57 (95% CI:1.48-4.43) and for ICU admission were 1.85 (95% CI:1.14-3.01) and 2.88 (95% CI:1.79-4.61), respectively. The optimal cut-off value of the initial NLR for predicting mortality was 4.27. Initial NLR can predict mortality and ICU admission in COVID-19 patients. Further investigations for curating the calculated cut-off can propose initial NLR as an indicator of poor prognosis for COVID-19 patients.

NTAM-LSTM models of network traffic prediction

Accurate prediction of network traffic is very important in allocating network resources. With the rapid development of network technology, network traffic becomes more complex and diverse. The traditional network traffic prediction model cannot accurately predict the current network traffic within the effective time. This paper proposes a Network Traffic Prediction Model----NTAM-LSTM, which based on Attention Mechanism with Long and Short Time Memory. Firstly, the model preprocesses the historical dataset of network traffic with multiple characteristics. Then the LSTM network is used to make initial prediction for the processed dataset. Finally, attention mechanism is introduced to get more accurate prediction results. Compared with other network traffic prediction models, NTAM-LSTM prediction model can achieve higher prediction accuracy and take shorter running time.

Self-Selection of Peers and Performance

This paper studies how the presence of peers and different peer assignment rules—self-selection versus random assignment—affect individual performance. Using a framed field experiment, we find that the presence of a randomly assigned peer improves performance by 28% of a standard deviation (SD), whereas self-selecting peers induces an additional 15%–18% SD improvement in performance. Our results document peer effects in multiple characteristics and show that self-selection changes these characteristics. However, a decomposition reveals that variations in the peer composition contribute only little to the performance differences across peer assignment rules. Rather, we find that self-selection has a direct effect on performance. This paper was accepted by Yan Chen, decision analysis.

Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

We introduce a novel three stepwise model of adaptive e-learning using multiple learner characteristics. We design a model of a learner attributes enlisting the study domain, summary details of the student and the requirements of the student. We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model. The affective state extraction model which extracts learner emotions from text inputs during the platform interactions. We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures and workshops depending on predefined constraints of learning. Simulated results show better adaptivity incases of multiple characteristics as opposed to single learner characteristics. Further research to include more than three characteristics as in this research.

Particular solutions of equations with multiple characteristics expressed through hypergeometric functions

In the paper, similarity solutions are constructed for a model equation with multiple characteristics of an arbitrary integer order. It is shown that the structure of similarity solutions depends on the mutual simplicity of the orders of derivatives with respect to the variable x and y, respectively. Frequent cases are considered in which they are shown as fundamental solutions of well-known equations, expressed in a linear way through the constructed similarity solutions.

Horizontal Spatial Metaphor Representation of Social Status in Chinese Culture

The mapping relationship between social status and horizontal space (left/right) in Chinese culture has a long history. In order to explore the representation pattern of horizontal spatial metaphor of social status in Chinese culture, this study introduced two direct measurements, implicit relational assessment procedure (IRAP) paradigm, and spatial placement task to evaluate the mapping of social status to horizontal space. A total of 144 Chinese undergraduates participated in the research, wherein they were asked to place certain words indicating social status in either left or right box before or after the IRAP computer test. The results from the two measurements consistently showed that the mode of HIGH SOCIAL STATUS-LEFT and LOW SOCIAL STATUS-RIGHT (HLLR) had an advantage over HIGH SOCIAL STATUS-RIGHT and LOW SOCIAL STATUS-LEFT (HRLL), implying that the representation pattern of horizontal spatial metaphor of social status for the Chinese is HIGH SOCIAL STATUS-LEFT and LOW SOCIAL STATUS-RIGHT. However, the result convergence of the two measurements was not high, which suggests that embodiment effect has multiple characteristics and new specific experimental paradigms should be created to measure it.

Differences in assistive technology installed for people with dementia living at home who have wandering and safety risks

Background Assistive Technology for people with dementia living at home is not meeting their care needs. Reasons for this may be due to limited understanding of variation in multiple characteristics of people with dementia including their safety and wandering risks, and how these affect their assistive technology requirements. This study therefore aimed to explore the possibility of grouping people with dementia according to data describing multiple person characteristics. Then to investigate the relationships between these groupings and installed Assistive Technology interventions. Methods Partitioning Around Medoids cluster analysis was used to determine participant groupings based upon secondary data which described the person characteristics of 451 people with dementia with Assistive Technology needs. Relationships between installed Assistive Technology and participant groupings were then examined. Results Two robust clustering solutions were identified within the person characteristics data. Relationships between the clustering solutions and installed Assistive Technology data indicate the utility of this method for exploring the impact of multiple characteristics on Assistive technology installations. Living situation and caregiver support influence installation of assistive technology more strongly than level of risk or cognitive impairment. People with dementia living alone received different AT from those living with others. Conclusions Results suggest that caregiver support and the living situation of the person with dementia influence the type and frequency of installed Assistive Technology. Reasons for this include the needs of the caregiver themselves, the caregiver view of the participants’ needs, caregiver response to alerts, and the caregiver contribution to the assistive technology assessment and selection process. Selection processes should be refined to account for the needs and views of both caregivers and people with dementia. This will require additional assessor training, and the development of validated assessments for people with dementia who have additional impairments. Policies should support the development of services which provide a wider range of AT to facilitate interventions which are focused on the needs of the person with dementia.

Strengths-Based Approaches to Conducting Research With Low-Income and Other Marginalized Populations

As researchers, providers, and policymakers strive to make their work more inclusive, it is important to move beyond simply paying more attention to “understudied” groups. Along with calls for more research, there should be equally vigorous calls to move beyond stigmatizing, deficits-based approaches and instead develop respectful, strengths-based lenses. One challenge is the lack of published guidance about how to interact professionally with marginalized populations. Guided by standpoint theory, I share my experiences and recommendations for working with marginalized populations, focusing especially on working with people from low-income and working-class communities. Everyone has a standpoint that is shaped by multiple characteristics, and for many people these will include some privileged and some disadvantaged characteristics. However, even for well-intentioned people, it can be challenging to look beyond one’s socialization and to recognize that personal characteristics, in and of themselves, cannot confer goodness on a person, only privilege. The following recommendations are explored in more detail in the chapter: researchers are encouraged to recognize one’s perspective (reflexivity), avoid setting up studies that inadvertently reflect academic culture, offer incentives that convey respect for people’s time and expertise in their own lives, ensure measures are written in straightforward (not academic) language, include or create measures that explore the strengths of marginalized groups, and disseminate work to communities, not just fellow researchers. Researchers, students, editors, reviewers, and policymakers should be aware of best practices in this area.

MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools

The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many of these tools are open-source and freely available but are very diverse with respect to language, data formats, and stages in the metabolomics pipeline. To help mitigate the challenges of meeting the increasing demand for guidance in choosing analytical tools and coordinating the adoption of best practices for reproducibility, we have designed and built the MSCAT (Metabolomics Software CATalog) database of metabolomics software tools that can be sustainably and continuously updated. This database provides a survey of the landscape of available tools and can assist researchers in their selection of data analysis workflows for metabolomics studies according to their specific needs. We used machine learning (ML) methodology for the purpose of semi-automating the identification of metabolomics software tool names within abstracts. MSCAT searches the literature to find new software tools by implementing a Named Entity Recognition (NER) model based on a neural network model at the sentence level composed of a character-level convolutional neural network (CNN) combined with a bidirectional long-short-term memory (LSTM) layer and a conditional random fields (CRF) layer. The list of potential new tools (and their associated publication) is then forwarded to the database maintainer for the curation of the database entry corresponding to the tool. The end-user interface allows for filtering of tools by multiple characteristics as well as plotting of the aggregate tool data to monitor the metabolomics software landscape.

Perspectivele microclonării și micropropagării speciei Rosa canina L.

Rosehip fruits are characterized by high concentrations of vitamin C and antioxidants, which we can say that rosehip is a reservoir of vitamins for the human body. The aim of this study is to improve the in vitro propagation efficiency of this valuable plant species. In this study, apical buds were inoculated on 100% MS medium supplemented with BAP of 0.5-1.0 mg/l and FeNaEDDHA 50 mg/l - Sequestrene 138, to avoid the phenomenon of leaf chlorosis. Due to the multiple characteristics, Rosa canina is of practical interest for our country, which leads to the need to produce propagating material through in vitro culture.